Tejo Vs Expensive Devices
Modern AI and camera technologies have dramatically advanced in the last decade
What Tejo Offers that Expensive Devices Can't
Virtual consultations
You can use Tejo as a virtual consultant, on your website to help guide new clients to the right service for them. This requires no work on the side of the spa, and prospective clients have a quick and easy way to connect with the spa. This can help the spa collect contact information of prospective clients as well as their biggest skin concerns.
Scan Before Treatments
When asking clients to check in for their appointment they can also scan their skin. This will provide more up to date information about the skin. This can help prevent any reactions and guarantee a seamless experience.
Education on Autopilot
Its impossible to inform clients about all the products you have on offer and clients often forget what was taught. Tejo offers the ability to explain automatically to clients what products and services they should use moving forward and WHY.
Understanding the Tech
Lighting
UV and Infrared Light
UV Light, used by devices like Wood Lamp are used primarily to detect [2]:
Bacteria and Fungus
We chose not to detect bacteria on the skin because of emerging research on the skin microbiome. instead we focus on signs of bad bacteria or infection.
Hypopigmentation and Hyperpigmentation
Tejo has detects both of these already, and is able to detect early signs of these, even when they are not visible
Infrared Light is used to largely detect for Inflammation in a spa setting.[3] Tejo's redness score correlates directly with inflammation. [4]
Tejo Detections For Skin
Acne
Acne can be accurately and quickly assessed from a picture External studies have found accuracy ratings as high as 99.4% accuracy when detecting acne. Acne type, and severity can both be accurately assessed.[5][6]
Fine Lines and Wrinkels
FIne Lines and wrinkles can be captured and measured using a camera using various techniques. [7]
Texture
Skin texture can be quickly and accurately assessed with a camera.[7][8]
Oil
There is fairly conclusive evidence that oily skin can be detected from a camera and that no special light or magnification is needed [7][8][9]
Skin Health
skin health is an overall assessment of how youthful and healthy the skin looks. Tejo uses an internal scale to rate images.[7]
Redness/Inflammation
inflammation is assessed using visual indicators like swelling, redness, or other signs of irritation on the skin. Tejo is effective at detecting these issues even with darker skin tones.[10]
Sun Damaage
Sun damage is a composite score looking at common signs of sun damage.
Hyperpigmentation
There is strong evidence that Hyperpigmentation can be assesed with a camera. Tejo Currently uses our own unige scoring system but is looking to implement the the MASI scoring system, soon.[11][12] Studies have found accuracy rates above 95% [13]
Under Eye Circles
under eye circles can be detected from a camera, the area around the eyes can be assessed
Evenness
This detection looks for uneven skin tone, not produced by hyperpigmentation or shadows
hydration
Coming soon
Pore Size
Coming Soon
Tejo Detection For Brow, Lash and Micro-pigmentation
Eye Shape
eye shape
Eye Set
Eye Set
Face Shape
Face Shape
Skin Tone
Skin Tone can be accurately assessed to determine shade matching
References
[1] Y. Yang et al., “Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review,” Biosensors and Bioelectronics, vol. 229, p. 115233, Jun. 2023, doi: https://doi.org/10.1016/j.bios.2023.115233.
[2] Cleveland Clinic, “Wood’s Lamp Examination: Skin Analysis Under UV Light,” Cleveland Clinic, Jun. 17, 2022. https://my.clevelandclinic.org/health/diagnostics/23292-woods-lamp-examination
[3] X. She, H. Lu, Q. Liu, P. Xie, and Q. Xia, “Dermatological infrared thermal imaging with human-machine interaction image diagnostics interface using DenseNet,” Journal of Radiation Research and Applied Sciences, vol. 17, no. 1, p. 100826, Mar. 2024, doi: https://doi.org/10.1016/j.jrras.2024.100826.
[4]Abhijit Achyut Gurjarpadhye, M. B. Parekh, Arita Dubnika, Jayakumar Rajadas, and M. Inayathullah, “Infrared Imaging Tools for Diagnostic Applications in Dermatology,” SM journal of clinical and medical imaging, vol. 1, no. 1, p. 1, Nov. 2015, Accessed: Mar. 02, 2025. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC4683617/
[5] Huynh, Quan Thanh, et al. “Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence.” Diagnostics (Basel, Switzerland), U.S. National Library of Medicine, 3 Aug. 2022, pmc.ncbi.nlm.nih.gov/articles/PMC9406819/.
[6] H. Wen et al., “Acne detection and severity evaluation with interpretable convolutional neural network models,” Technology and Health Care, vol. 30, pp. 143–153, Feb. 2022, doi: https://doi.org/10.3233/thc-228014.
[7] J. de, L. Kakuda, and M. Berardo, “Morphological characteristics of normal and oily skin in different phototypes,” International Journal of Cosmetic Science, Feb. 2025, doi: https://doi.org/10.1111/ics.13049.
[8] D. G. Mercurio, J. H. Segura, M. B. A. Demets, and P. M. B. G. Maia Campos, “Clinical scoring and instrumental analysis to evaluate skin types,” Clinical and Experimental Dermatology, vol. 38, no. 3, pp. 302–309, Mar. 2013, doi: https://doi.org/10.1111/ced.12105.
[9] I. Kohli et al., “Quantitative measurement of skin surface oiliness and shine using differential polarized images,” Archives of Dermatological Research, Apr. 2020, doi: https://doi.org/10.1007/s00403-020-02070-5.
[10] J. Frew et al., “The erythema Q‐score, an imaging biomarker for redness in skin inflammation,” Experimental Dermatology, vol. 30, no. 3, pp. 377–383, Nov. 2020, doi: https://doi.org/10.1111/exd.14224.
[11] K. W. Gossage, J. Weissman, and R. Velthuizen, “Segmentation of hyper-pigmented spots in human skin using automated cluster analysis,” Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE, vol. 7161, pp. 71610A71610A, Feb. 2009, doi: https://doi.org/10.1117/12.809775.
[12] A. G. Pandya et al., “Reliability assessment and validation of the Melasma Area and Severity Index (MASI) and a new modified MASI scoring method,” Journal of the American Academy of Dermatology, vol. 64, no. 1, pp. 78-83.e2, Jan. 2011, doi: https://doi.org/10.1016/j.jaad.2009.10.051.
[13] H. Ding et al., “Automatic identification of benign pigmented skin lesions from clinical images using deep convolutional neural network,” BMC Biotechnology, vol. 22, no. 1, Oct. 2022, doi: https://doi.org/10.1186/s12896-022-00755-5.